Neural Computing for Determining the Accuracy of Ultimate Tensile Strength of Friction Stir Welded Joints by using Various Activation Functions
Activation functions in a particular Artificial Neural Network (ANN) architecture plays a vital role. It imparts non-linear properties to our Neural Networks. There is a complicated and non-linear complex functional mapping between the inputs and response variable. In our present work, we have focussed on the accuracy of the UTS of the dissimilar Friction Stir Welded joints obtained by the training and testing the Artificial Neural Network architecture on Sigmoid activation function, Rectified Linear unit (ReLu) activation function and Hyperbolic tangent activation function. Tool Rotational Speed (rpm), Welding speed (mm/min) are the inputs and Ultimate Tensile Strength (MPa) is the output in our neural network architecture.
How to cite this article:
Mishra A. Neural Computing for Determining the Accuracy of Ultimate Tensile Strength of Friction Stir Welded Joints by using Various Activation Functions. J Adv Res Mech Engi Tech 2019; 6(1&2): 27-31.
2. Yao X. Evolving artificial neural networks. Proceedings of the IEEE 1999; 87(9): 1423-1447.
3. Zhang G, Patuwo BE, Hu MY. Forecasting with artificial neural networks: The state of the art. International
journal of forecasting 1998; 14(1): 35-62.
4. Mishra RS, Ma ZY. Friction stir welding and processing. Materials science and engineering: R: reports, 2005;
5. Anand K, Barik BK, Tamilmannan K et al. Artificial neural network modeling studies to predict the friction welding process parameters of Incoloy 800H joints. Engineering Science and Technology, an International
Journal 2015; 18(3): 394-407.
6. Fratini L, Buffa G, Palmeri D. Using a neural network for predicting the average grain size in friction stir welding processes. Computers & Structures 2009; 87(17-18): 1166-1174.
7. Al-Ghazaly SB, Al-shafaie SH. Optimization of Friction Stir Welding Parameters of Al 6061 and Al 7075 Using
GRA. JUBES 2018; 26(5): 147.